entry_point stringlengths 1 65 | original_triton_python_code stringlengths 208 619k | optimised_triton_code stringlengths 1.15k 275k | repo_name stringlengths 7 115 | module_name stringlengths 1 65 | synthetic bool 1
class | uuid int64 0 18.5k | licenses listlengths 1 6 | stars int64 0 19.8k | sha stringlengths 40 40 | repo_link stringlengths 72 180 |
|---|---|---|---|---|---|---|---|---|---|---|
MeanMaxPooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empt... | jennybae1024/DFGN-pytorch | MeanMaxPooling | false | 15,681 | [
"MIT"
] | 191 | 056d9317f772cd10bdd215bfafdbac5cbd330026 | https://github.com/jennybae1024/DFGN-pytorch/tree/056d9317f772cd10bdd215bfafdbac5cbd330026 |
SpatialSELayer1d | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ioanvl/1d_squeeze_excitation | SpatialSELayer1d | false | 10,245 | [
"MIT"
] | 0 | f422dc4b8e7de6239a6fb7d1688048db5053e733 | https://github.com/ioanvl/1d_squeeze_excitation/tree/f422dc4b8e7de6239a6fb7d1688048db5053e733 |
CosineClassifier | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | ZRJMoon/OMIT | CosineClassifier | false | 9,630 | [
"MIT"
] | 0 | bb063b4ac5d4fd60b28b17cb8d2119da92f936f4 | https://github.com/ZRJMoon/OMIT/tree/bb063b4ac5d4fd60b28b17cb8d2119da92f936f4 |
SE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Alessiacosmos/Basic-RegNet-pytorch | SE | false | 18,408 | [
"MIT"
] | 2 | fd6b9a67599dcea6c90ba247f532a7624252b33c | https://github.com/Alessiacosmos/Basic-RegNet-pytorch/tree/fd6b9a67599dcea6c90ba247f532a7624252b33c |
Dummy | import torch
from torch import nn
class Dummy(nn.Module):
def forward(self, input):
x = input
return x + 1
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | FynnBe/tiktorch | Dummy | false | 11,430 | [
"MIT"
] | 0 | 60c6fa9700e7ff73e44338e8755c56c6e8846f2f | https://github.com/FynnBe/tiktorch/tree/60c6fa9700e7ff73e44338e8755c56c6e8846f2f |
Transform | import torch
from torch import nn
class Transform(nn.Module):
"""
A Vertex Transformation module
Permutation invariant transformation: (N, k, d) -> (N, k, d)
"""
def __init__(self, dim_in, k):
"""
:param dim_in: input feature dimension
:param k: k neighbors
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | weiyx15/DHGNN | Transform | false | 16,711 | [
"MIT"
] | 124 | 870a1763c34af6ee9a7a3207fed4a5e6bdb95d23 | https://github.com/weiyx15/DHGNN/tree/870a1763c34af6ee9a7a3207fed4a5e6bdb95d23 |
SimpleFloorModule | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._... | YaronBenAtar/glow | SimpleFloorModule | false | 14,661 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
MaxPoolStride1 | import torch
import torch.nn as nn
import torch.nn.functional as F
class MaxPoolStride1(nn.Module):
def __init__(self):
super(MaxPoolStride1, self).__init__()
def forward(self, x):
x_pad = F.pad(x, (0, 1, 0, 1), mode='replicate')
x = F.max_pool2d(x_pad, 2, stride=1)
return x
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | CharlesPikachu/YOLO | MaxPoolStride1 | false | 13,452 | [
"MIT"
] | 57 | 950b11c35517c1c3d7d7856b5768c4023c1f89eb | https://github.com/CharlesPikachu/YOLO/tree/950b11c35517c1c3d7d7856b5768c4023c1f89eb |
Theta | from torch.autograd import Function
import torch
import torch.nn as nn
from typing import Tuple
from typing import Optional
from typing import Any
import torch.utils.data
class GradientReverseFunction(Function):
@staticmethod
def forward(ctx: 'Any', input: 'torch.Tensor', coeff: 'Optional[float]'=1.0
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch.autograd import Function
import torch.nn as nn
from typing import Tup... | XianyuanLiu/Transfer-Learning-Library | Theta | false | 10,142 | [
"MIT"
] | 0 | 25f83f32437032df88ca6101ecd1f63ec7a0aa2c | https://github.com/XianyuanLiu/Transfer-Learning-Library/tree/25f83f32437032df88ca6101ecd1f63ec7a0aa2c |
HardtanhBoundToPOTNet | import torch
from torch.nn.functional import relu
from torch.nn import Conv2d
from torch.nn import Hardtanh
from torch.nn.functional import hardtanh
import torch.nn.functional
class HardtanhBoundToPOTNet(torch.nn.Module):
def __init__(self):
super(HardtanhBoundToPOTNet, self).__init__()
self.conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch.nn import Conv2d
f... | elad-c/model_optimization | HardtanhBoundToPOTNet | false | 10,658 | [
"Apache-2.0"
] | 0 | b0ecf41c3f9434008d57d7fe724ff8585e19d4cc | https://github.com/elad-c/model_optimization/tree/b0ecf41c3f9434008d57d7fe724ff8585e19d4cc |
GKDLoss | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.nn.functional as F
class GKDLoss(nn.Module):
"""Knowledge Distillation Loss"""
def __init__(self, T):
super().__init__()
self.t = T
def forward(self, stu_pred, tea_pred, label):
stu_pred_l... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | carol007/pytorch-ImageNet-CIFAR-COCO-VOC-training | GKDLoss | false | 6,402 | [
"MIT"
] | 1 | e8b37046e6fbe914f6a68bbde1fe419c46373c1d | https://github.com/carol007/pytorch-ImageNet-CIFAR-COCO-VOC-training/tree/e8b37046e6fbe914f6a68bbde1fe419c46373c1d |
ScaledLeakyReLU | import math
import torch
import torch.nn as nn
from torch.nn import functional as F
class ScaledLeakyReLU(nn.Module):
"""Scaled LeakyReLU.
Args:
negative_slope (float): Negative slope. Default: 0.2.
"""
def __init__(self, negative_slope=0.2):
super(ScaledLeakyReLU, self).__init__()
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | ArdWang/GFPGAN | ScaledLeakyReLU | false | 11,243 | [
"BSD-3-Clause"
] | 0 | f984ec32754190fad0b9b7a60d372aac84e57173 | https://github.com/ArdWang/GFPGAN/tree/f984ec32754190fad0b9b7a60d372aac84e57173 |
PolicyAHG | import torch
import numpy as np
import torch as tor
from torch import nn
class SaveableModel(object):
def save(self, path):
tor.save(self, path)
@classmethod
def load(cls, path):
return tor.load(path)
@classmethod
def load_best(cls, path):
assert os.path.isdir(path)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JimmyMVP/plain_rl | PolicyAHG | false | 17,495 | [
"MIT"
] | 10 | 4780f05fffb62533a339197b49de487cdc9d9954 | https://github.com/JimmyMVP/plain_rl/tree/4780f05fffb62533a339197b49de487cdc9d9954 |
Flatten | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | DeepVoodooFX/pixel2style2pixel | Flatten | false | 11,341 | [
"Apache-2.0",
"BSD-2-Clause",
"MIT"
] | 0 | 0254c32400d55f7e400ead15b02ad6a992ba1e21 | https://github.com/DeepVoodooFX/pixel2style2pixel/tree/0254c32400d55f7e400ead15b02ad6a992ba1e21 |
GraphLinear | import torch
import torch.nn as nn
class GraphLinear(nn.Module):
"""
Generalization of 1x1 convolutions on Graphs
"""
def __init__(self, in_channels, out_channels):
super(GraphLinear, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | GentleDell/DEBOR | GraphLinear | false | 17,295 | [
"BSD-3-Clause"
] | 4 | cd566f173599fe7419e7baf312f63830c28d5de2 | https://github.com/GentleDell/DEBOR/tree/cd566f173599fe7419e7baf312f63830c28d5de2 |
PointerHead | import torch
import torch.quantization
from torch import nn
class PointerHead(nn.Module):
"""Head for pointer ordering task."""
def __init__(self, embed_dim, bias=True):
super().__init__()
self.embed_dim = embed_dim
self.scaling = self.embed_dim ** -0.5
self.k_proj = nn.Linear... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.quantization
from torch import nn
assert_size_stride = torch._C._dy... | airKlizz/passage-ordering | PointerHead | false | 6,132 | [
"MIT"
] | 1 | f63b993dfd5b7e6475e7fb8950c23c3f22951979 | https://github.com/airKlizz/passage-ordering/tree/f63b993dfd5b7e6475e7fb8950c23c3f22951979 |
CRF | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data.dataloader
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | OatsProduction/flair | CRF | false | 11,760 | [
"MIT"
] | 0 | 1cf2c9a9ae487e279dce9f6b92c41fa32c4563cf | https://github.com/OatsProduction/flair/tree/1cf2c9a9ae487e279dce9f6b92c41fa32c4563cf |
BertSelfAttention | from _paritybench_helpers import _mock_config
import math
import torch
from torch import nn
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | johnson7788/TextBrewer | BertSelfAttention | false | 6,983 | [
"Apache-2.0"
] | 1 | fa7fa4d4a2a8debde5b148d448238f3b4fa1aa9a | https://github.com/johnson7788/TextBrewer/tree/fa7fa4d4a2a8debde5b148d448238f3b4fa1aa9a |
MultiHeadAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | JINHXu/NeMo | MultiHeadAttention | false | 11,637 | [
"Apache-2.0"
] | 0 | 835db62e39919436824ce022fd3b3f6bac301cd6 | https://github.com/JINHXu/NeMo/tree/835db62e39919436824ce022fd3b3f6bac301cd6 |
LBM | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | jinfenglin/TaxoExpan | LBM | false | 15,688 | [
"Apache-2.0"
] | 55 | 86bd3f805508d03367539f2fdd43889fc0a4f6b2 | https://github.com/jinfenglin/TaxoExpan/tree/86bd3f805508d03367539f2fdd43889fc0a4f6b2 |
LogCoshLoss | import torch
import torch.utils.cpp_extension
class LogCoshLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, true, pred):
loss = true - pred
return torch.mean(torch.log(torch.cosh(loss + 1e-12)))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | crobbins327/semanticGAN_WSI | LogCoshLoss | false | 1,749 | [
"BSD-2-Clause",
"MIT"
] | 0 | 4046ddc822f463e03952402247f79d540bf7be95 | https://github.com/crobbins327/semanticGAN_WSI/tree/4046ddc822f463e03952402247f79d540bf7be95 |
ResNetV2 | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Willy0919/progressive-coordinate-transforms | ResNetV2 | false | 14,819 | [
"Apache-2.0",
"MIT"
] | 142 | b637fa2541a815d270e162a4c9cd3348b098d48a | https://github.com/Willy0919/progressive-coordinate-transforms/tree/b637fa2541a815d270e162a4c9cd3348b098d48a |
JaccardLoss | import torch
import torch.nn as nn
class JaccardLoss(nn.Module):
def __init__(self, apply_softmax: 'bool'=False, eps: 'float'=1e-06):
super().__init__()
self.apply_softmax = apply_softmax
self.eps = eps
def forward(self, x, y, eps=1e-06):
if self.apply_softmax:
x ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | HalestormAI/efficientnet-unet | JaccardLoss | false | 2,334 | [
"MIT"
] | 0 | b6d5ec86d667ce7ac1f689bc16269dca83a079f0 | https://github.com/HalestormAI/efficientnet-unet/tree/b6d5ec86d667ce7ac1f689bc16269dca83a079f0 |
ConditionTime | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._emp... | ValterFallenius/metnet | ConditionTime | false | 9,652 | [
"MIT"
] | 0 | 7cde48a7b5fc0b69a8ce9083f934949362620fd5 | https://github.com/ValterFallenius/metnet/tree/7cde48a7b5fc0b69a8ce9083f934949362620fd5 |
ConvRelu | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
from to... | asmekal/catalyst | ConvRelu | false | 12,122 | [
"MIT"
] | 0 | e11365c0a9812649ceaef14e53061cd5117d8684 | https://github.com/asmekal/catalyst/tree/e11365c0a9812649ceaef14e53061cd5117d8684 |
TVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | myeldib/Simple-SR | TVLoss | false | 12,808 | [
"MIT"
] | 0 | 583456b1f231574d9e0b45c29266cf41603d161d | https://github.com/myeldib/Simple-SR/tree/583456b1f231574d9e0b45c29266cf41603d161d |
CBAM_Module | import torch
from typing import *
import torch.nn as nn
class CBAM_Module(nn.Module):
def __init__(self, channels, reduction):
super(CBAM_Module, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.max_pool = nn.AdaptiveMaxPool2d(1)
self.fc1 = nn.Conv2d(channels, channel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from typing import *
import t... | artyompal/kaggle_quick_draw | CBAM_Module | false | 3,136 | [
"Apache-2.0"
] | 0 | 227e228295479cd5e1af8dcde773f5efdacd62b8 | https://github.com/artyompal/kaggle_quick_draw/tree/227e228295479cd5e1af8dcde773f5efdacd62b8 |
Embedding | import torch
import numpy as np
import torch as t
import torch.nn as nn
import torch.utils.data
class Embedding(nn.Module):
"""
Redefining torch.nn.Embedding (see docs for that function)
"""
def __init__(self, num_embeddings, embedding_dim, padding_idx=None,
_weight=None):
super().__i... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import numpy as np
import torch as t
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_... | arjunsesh/lrr-neurips | Embedding | false | 6,232 | [
"MIT"
] | 1 | d78106daec1e729b02a0452f74a37bf004ed243c | https://github.com/arjunsesh/lrr-neurips/tree/d78106daec1e729b02a0452f74a37bf004ed243c |
length_evolution | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | LiWentomng/boxlevelset | length_evolution | false | 8,461 | [
"Apache-2.0"
] | 25 | 8cc40bf6ae4a343c482c676c72259cc12c29d31c | https://github.com/LiWentomng/boxlevelset/tree/8cc40bf6ae4a343c482c676c72259cc12c29d31c |
ConvTran | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | RicCu/NeuralStyle | ConvTran | false | 5,767 | [
"MIT"
] | 1 | 97dc6aec6b2072a9a187276e047aea885566e1be | https://github.com/RicCu/NeuralStyle/tree/97dc6aec6b2072a9a187276e047aea885566e1be |
FasterBlock | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | becauseofAI/DemoHub | FasterBlock | false | 3,202 | [
"Apache-2.0"
] | 0 | 2b7fdd1f1c6f229ba326e8c1b78c4e7f5982f3da | https://github.com/becauseofAI/DemoHub/tree/2b7fdd1f1c6f229ba326e8c1b78c4e7f5982f3da |
ResARModule | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.spectral_norm import spectral_norm
def build_norm_layer(norm_type, param=None, num_feats=None):
if norm_type == 'bnorm':
return nn.BatchNorm1d(num_feats)
elif norm_type == 'snorm':
spectral_norm(param)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.nn.utils.spectral_norm import spectral_norm
ass... | BaiYuhaoSpiceeYJ/SEGAN_denoise | ResARModule | false | 2,025 | [
"MIT"
] | 0 | 5bf65ae72b9f0a996ae338c53c68c4967e08cd59 | https://github.com/BaiYuhaoSpiceeYJ/SEGAN_denoise/tree/5bf65ae72b9f0a996ae338c53c68c4967e08cd59 |
GaussianFocalLoss | import functools
import torch
import torch.nn.functional as F
import torch.nn as nn
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | Guoning-Chen/mmdetection | GaussianFocalLoss | false | 503 | [
"Apache-2.0"
] | 0 | f1d1c5a19dbe6aa2e74fc9ca2e9578db4532fc64 | https://github.com/Guoning-Chen/mmdetection/tree/f1d1c5a19dbe6aa2e74fc9ca2e9578db4532fc64 |
KL_loss_softmax | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | BruceW91/CVSE | KL_loss_softmax | false | 13,414 | [
"MIT"
] | 152 | 20fa1ff50d1dcb4a7b3799071fa78038e52db804 | https://github.com/BruceW91/CVSE/tree/20fa1ff50d1dcb4a7b3799071fa78038e52db804 |
NTXent | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class NTXent(nn.Module):
"""
Contrastive loss with distributed data parallel support
code: https://github.com/AndrewAtanov/simclr-pytorch/blob/master/models/losses.py
"""
LARGE_NUMBER = 1000000000.0
def __in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | salesforce/CoSeRec | NTXent | false | 10,765 | [
"BSD-3-Clause"
] | 0 | c0bf5e5c3a5fd645efd3d6cdb9ff6a98d1c477ef | https://github.com/salesforce/CoSeRec/tree/c0bf5e5c3a5fd645efd3d6cdb9ff6a98d1c477ef |
Upsample | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AkioHayakawa/SDEdit | Upsample | false | 1,938 | [
"MIT"
] | 0 | 54d793bc013ea99ae81c539bc559254fa8746e19 | https://github.com/AkioHayakawa/SDEdit/tree/54d793bc013ea99ae81c539bc559254fa8746e19 |
DepthwiseSeparableConv2d | import torch
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.utils.data
from torchvision.transforms import *
class DepthwiseSeparableConv2d(nn.Module):
def __init__(self, input, output, padding=0, bias=False):
super(DepthwiseSeparableConv2d, self).__init__()
self.depthwise = nn... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.optim.lr_scheduler
import torch.utils.data
fr... | csharpshooter/DeepLearning | DepthwiseSeparableConv2d | false | 1,775 | [
"MIT"
] | 0 | c1d20660c32076468970f7376931e1fcd0d2644e | https://github.com/csharpshooter/DeepLearning/tree/c1d20660c32076468970f7376931e1fcd0d2644e |
OuterProductLayer | import torch
import torch.nn as nn
class OuterProductLayer(nn.Module):
"""OutterProduct Layer used in PNN. This implemention is
adapted from code that the author of the paper published on https://github.com/Atomu2014/product-nets.
"""
def __init__(self, num_feature_field, embedding_size, device):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | BELIEVEfxy/LightSANs | OuterProductLayer | false | 7,773 | [
"MIT"
] | 17 | 94ce7e59d144dbc787153b8c486cad334790ec6e | https://github.com/BELIEVEfxy/LightSANs/tree/94ce7e59d144dbc787153b8c486cad334790ec6e |
RefineModelReLU | import torch
import torch.nn as nn
class RefineModelReLU(torch.nn.Module):
def __init__(self, in_channels):
super(RefineModelReLU, self).__init__()
self.layer1 = nn.Linear(in_channels, 128)
self.relu1 = nn.ReLU()
self.layer2 = nn.Linear(128, 64)
self.relu2 = nn.ReLU()
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | BoyuanChen/neural-state-variables | RefineModelReLU | false | 7,854 | [
"MIT"
] | 17 | 10483d93ac8c006f3786c434fb57d70d9ab465ec | https://github.com/BoyuanChen/neural-state-variables/tree/10483d93ac8c006f3786c434fb57d70d9ab465ec |
AttNLocalNew | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
@triton.jit
def triton_poi_fused_index_put_lift_fres... | napoler/tkit-attnlocal-pytorch | AttNLocalNew | false | 4,044 | [
"Apache-2.0"
] | 0 | ec1c32cb49635824f978b3ec19b4c80505ea735b | https://github.com/napoler/tkit-attnlocal-pytorch/tree/ec1c32cb49635824f978b3ec19b4c80505ea735b |
InstanceNormalization | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | ImageProcessingCentraleLille2021/fast-neural-style | InstanceNormalization | false | 13,830 | [
"MIT"
] | 350 | e77456c35c2a49f90227119d158828a0964c7e13 | https://github.com/ImageProcessingCentraleLille2021/fast-neural-style/tree/e77456c35c2a49f90227119d158828a0964c7e13 |
FinalLayer | import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNorm(nn.Module):
def __init__(self, features, eps=1e-06):
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(features))
self.beta = nn.Parameter(torch.zeros(features))
self.eps = eps
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ishine/RPN_KWS | FinalLayer | false | 15,640 | [
"MIT"
] | 53 | b54d4010a701a6ec0a9ddf3ab6177a4be6dd6af5 | https://github.com/ishine/RPN_KWS/tree/b54d4010a701a6ec0a9ddf3ab6177a4be6dd6af5 |
CrossAttN_v8 | import torch
import torch.nn as nn
import torch.nn.functional as Func
class CrossAttN_v8(nn.Module):
def __init__(self, in_planes, clip_dim):
super(CrossAttN_v8, self).__init__()
self.f = nn.Conv2d(in_planes, in_planes, 1, 1, 0)
self.g = nn.Conv2d(in_planes, in_planes, 1, 1, 0)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Holmes-Alan/TxST | CrossAttN_v8 | false | 9,237 | [
"MIT"
] | 0 | c5b59a12bbb9e62244c3b608581d5cb9606525e0 | https://github.com/Holmes-Alan/TxST/tree/c5b59a12bbb9e62244c3b608581d5cb9606525e0 |
GRUCell | import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
class GRUCell(nn.Module):
def __init__(self, input_size, hidden_size, bias=True):
super(GRUCell, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.bias = bias
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import numpy as np
... | YilliaJing/PM2.5-GNN | GRUCell | false | 14,692 | [
"MIT"
] | 91 | 7aacc6b6b9562ad2a9dad6197e6c4d73607ebdf2 | https://github.com/YilliaJing/PM2.5-GNN/tree/7aacc6b6b9562ad2a9dad6197e6c4d73607ebdf2 |
DiceLoss | import torch
import torch.nn as nn
import torch.utils.data
def flatten_samples(input_):
"""
Flattens a tensor or a variable such that the channel axis is first and the sample axis
is second. The shapes are transformed as follows:
(N, C, H, W) --> (C, N * H * W)
(N, C, D, H, W) --> (C, N * ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guard... | JoOkuma/torch-em | DiceLoss | false | 664 | [
"MIT"
] | 0 | 68b723683f9013723a0e4fc8cfef1d6a2a9c9dff | https://github.com/JoOkuma/torch-em/tree/68b723683f9013723a0e4fc8cfef1d6a2a9c9dff |
FocalFrequencyLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C.... | EndlessSora/focal-frequency-loss | FocalFrequencyLoss | false | 13,645 | [
"MIT"
] | 364 | dcaa01ecbfbbd9d8f83f7e5993474e1aa087227c | https://github.com/EndlessSora/focal-frequency-loss/tree/dcaa01ecbfbbd9d8f83f7e5993474e1aa087227c |
EqualConvTranspose2d | import math
import torch
import torch.nn.functional as F
from torch import nn
class EqualConvTranspose2d(nn.Module):
def __init__(self, in_channel, out_channel, kernel_size, stride=1,
padding=0, bias=True):
super().__init__()
self.weight = nn.Parameter(torch.randn(in_channel, out_channel,... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.as... | xh-liu-tech/CIPS-3D | EqualConvTranspose2d | false | 11,108 | [
"MIT"
] | 0 | 8910dfcf19bb86aab2287d652ae4e3666806b511 | https://github.com/xh-liu-tech/CIPS-3D/tree/8910dfcf19bb86aab2287d652ae4e3666806b511 |
FilterResponseNormNd | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | aouedions11/SSFL-Benchmarking-Semi-supervised-Federated-Learning | FilterResponseNormNd | false | 6,234 | [
"MIT"
] | 1 | 78aec81919bf95ed4677d0e0a4ebbbe3be455742 | https://github.com/aouedions11/SSFL-Benchmarking-Semi-supervised-Federated-Learning/tree/78aec81919bf95ed4677d0e0a4ebbbe3be455742 |
SpatialGatingUnit | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | nima1999nikkhah/gMLP | SpatialGatingUnit | false | 12,833 | [
"MIT"
] | 0 | 6e04a173bdb137680695fe55753d8b2284f03fa4 | https://github.com/nima1999nikkhah/gMLP/tree/6e04a173bdb137680695fe55753d8b2284f03fa4 |
SE | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_st... | mengzhu0308/EfficientNetV2-PyTorch | SE | false | 12,777 | [
"Apache-2.0"
] | 0 | b9946a4372849d9231a044dcbf697ae17008b467 | https://github.com/mengzhu0308/EfficientNetV2-PyTorch/tree/b9946a4372849d9231a044dcbf697ae17008b467 |
DenseNet2D_up_block_concat | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | ArmandB/RITnet | DenseNet2D_up_block_concat | false | 91 | [
"MIT"
] | 0 | afe9524fdd795982c7e52761da68af2dfda589ea | https://github.com/ArmandB/RITnet/tree/afe9524fdd795982c7e52761da68af2dfda589ea |
EncoderImagePrecomp | import torch
import numpy as np
from collections import OrderedDict
import torch.nn as nn
import torch.nn.init
def l2norm(x, dim=-1):
return x / x.norm(2, dim=dim, keepdim=True).clamp(min=1e-06)
class EncoderImagePrecomp(nn.Module):
""" image encoder """
def __init__(self, img_dim, embed_size, no_imgno... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | davidatbu/MLVGSNL | EncoderImagePrecomp | false | 15,131 | [
"MIT"
] | 97 | 88d42424a0a7badb43e22cd3950948c9522faaa1 | https://github.com/davidatbu/MLVGSNL/tree/88d42424a0a7badb43e22cd3950948c9522faaa1 |
_BoundaryRefineModule | import torch
from torch import nn
class _BoundaryRefineModule(nn.Module):
def __init__(self, dim):
super(_BoundaryRefineModule, self).__init__()
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(dim, dim, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(dim, dim, kernel_siz... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | adynathos/pytorch-semantic-segmentation | _BoundaryRefineModule | false | 9,930 | [
"MIT"
] | 0 | 44d1784984cfd0926821c3fdbc20d371bb074296 | https://github.com/adynathos/pytorch-semantic-segmentation/tree/44d1784984cfd0926821c3fdbc20d371bb074296 |
Loss | import torch
import torch.nn as nn
class Loss(nn.Module):
def __init__(self):
super(Loss, self).__init__()
self.BCELoss = nn.BCELoss(reduce=True, size_average=True)
def forward(self, predict_y, input_y):
loss = self.BCELoss(predict_y, input_y)
return loss
def get_inputs():
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | RandolphVI/HyperNet | Loss | false | 5,762 | [
"Apache-2.0"
] | 1 | e9f376f5eb087e57360ca41cca2533c3ca967e47 | https://github.com/RandolphVI/HyperNet/tree/e9f376f5eb087e57360ca41cca2533c3ca967e47 |
TargetContextGate | import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
class ContextGate(nn.Module):
"""
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and
produces a gate.
The gate can be used to select t... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | ESCM-summarization/ESCM-summary-evaluation | TargetContextGate | false | 9,119 | [
"MIT"
] | 0 | 3780b51f0ed44cbbea3f163a871d875f1e5e9393 | https://github.com/ESCM-summarization/ESCM-summary-evaluation/tree/3780b51f0ed44cbbea3f163a871d875f1e5e9393 |
TransformerEncoderLayer | from torch.nn import Module
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Linear
from torch.nn import Dropout
from torch.nn import LayerNorm
from torch.nn import Identity
def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
"""
Obtained from: github.com:r... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | iliasprc/Compact-Transformers | TransformerEncoderLayer | false | 3,685 | [
"Apache-2.0"
] | 0 | 31975a0b4469854dfb0e0cbcedd8f0698cf84a7e | https://github.com/iliasprc/Compact-Transformers/tree/31975a0b4469854dfb0e0cbcedd8f0698cf84a7e |
TVLoss | import torch
class TVLoss(torch.nn.Module):
def __init__(self):
super(TVLoss, self).__init__()
def forward(self, x):
x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
self._tensor_size(x[:, :, 1:, :])
self._tensor_size(x[:, :, :, 1:])
h_tv = torch.pow(x[... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | Holmes-Alan/Photo2Sketch | TVLoss | false | 530 | [
"MIT"
] | 0 | 43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 | https://github.com/Holmes-Alan/Photo2Sketch/tree/43a0ca6bb8a8e645b35a2ab23d11ed5efe117e09 |
MinimalRNNCell | import torch
from torch import nn
from functools import partial
def get_initializer(name, activation):
if activation in ['id', 'identity', 'linear', 'modrelu']:
nonlinearity = 'linear'
elif activation in ['relu', 'tanh', 'sigmoid']:
nonlinearity = activation
else:
assert False, f'g... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
from torch import n... | tarepan/HiPPO | MinimalRNNCell | false | 16,539 | [
"Apache-2.0"
] | 57 | bc23e2dba13da6c307cb5a4ae248c2d2c56d465f | https://github.com/tarepan/HiPPO/tree/bc23e2dba13da6c307cb5a4ae248c2d2c56d465f |
SoftCrossEntropyLoss2d | import torch
import torch.utils.data.distributed
import torch
import torch.nn as nn
from numpy import int64 as int64
from torchvision.transforms import functional as F
import torch.nn.functional as F
import torch.utils
class SoftCrossEntropyLoss2d(nn.Module):
def __init__(self):
super(SoftCrossEntropyLos... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | HRTNet/HRTNet | SoftCrossEntropyLoss2d | false | 898 | [
"MIT"
] | 0 | 6a51c9c34568988ea6125a1638794c63d8fadbea | https://github.com/HRTNet/HRTNet/tree/6a51c9c34568988ea6125a1638794c63d8fadbea |
ConvSig | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import math
import torch.nn.functional as F
from torch.nn import Conv2d
from tor... | fish258/MonoRec | ConvSig | false | 15,366 | [
"MIT"
] | 388 | c0612d2710802004cdd83205e63d0582de543c41 | https://github.com/fish258/MonoRec/tree/c0612d2710802004cdd83205e63d0582de543c41 |
weightedFeatureFusion | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Nigel233/Different-Backbones-for-YOLO-v3 | weightedFeatureFusion | false | 9,360 | [
"MIT"
] | 0 | 030e7860e966b079afc9b53a320a41f3eb7950be | https://github.com/Nigel233/Different-Backbones-for-YOLO-v3/tree/030e7860e966b079afc9b53a320a41f3eb7950be |
LogCoshLoss | import torch
def apply_reduction(losses, reduction='none'):
"""Apply reduction to collection of losses."""
if reduction == 'mean':
losses = losses.mean()
elif reduction == 'sum':
losses = losses.sum()
return losses
class LogCoshLoss(torch.nn.Module):
"""Log-cosh loss function mod... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_strid... | leoauri/auraloss | LogCoshLoss | false | 15,904 | [
"Apache-2.0"
] | 272 | 0e3362674ae1b53aa61c6a631fb4e6970c5683c1 | https://github.com/leoauri/auraloss/tree/0e3362674ae1b53aa61c6a631fb4e6970c5683c1 |
AngularLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import math... | donikv/rgn_pytorch | AngularLoss | false | 1,863 | [
"MIT"
] | 0 | 95f8cd36fec5655f9bfd8634fff89b06e81dc2ed | https://github.com/donikv/rgn_pytorch/tree/95f8cd36fec5655f9bfd8634fff89b06e81dc2ed |
CPAMDec | from torch.nn import Module
import torch
from torchvision.datasets import *
from torch.nn import Parameter
from torch.nn import Conv2d
from torch.nn import Linear
from torch.nn import Softmax
from torchvision.transforms import *
class CPAMDec(Module):
"""
CPAM decoding module
"""
def __init__(self, i... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | tousifulhaque/DANet | CPAMDec | false | 4,460 | [
"MIT"
] | 0 | 1a0c91f0e551a071b5e335b4157313780a8a1b1a | https://github.com/tousifulhaque/DANet/tree/1a0c91f0e551a071b5e335b4157313780a8a1b1a |
CharbonnierLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
impo... | EvgeneyZ/TMNet | CharbonnierLoss | false | 13,649 | [
"Apache-2.0"
] | 90 | 8a42754747c2fa575e9108c13b5018a884f46099 | https://github.com/EvgeneyZ/TMNet/tree/8a42754747c2fa575e9108c13b5018a884f46099 |
DiscreteNet | import torch
import torch.nn as nn
import torch.nn.functional as F
def set_init(layers):
for layer in layers:
nn.init.normal_(layer.weight, mean=0.0, std=0.1)
nn.init.constant_(layer.bias, 0.0)
class DiscreteNet(nn.Module):
def __init__(self, s_dim, a_dim):
super(DiscreteNet, self).... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | lws803/pytorch-A3C | DiscreteNet | false | 10,458 | [
"MIT"
] | 0 | 944e7f42a8fa54b7d6efbe169d8a3467b20a0f7f | https://github.com/lws803/pytorch-A3C/tree/944e7f42a8fa54b7d6efbe169d8a3467b20a0f7f |
RAddInt | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | NVIDIA-AI-IOT-private/torch2trt | RAddInt | false | 10,543 | [
"MIT"
] | 0 | 953d60039e0c81e90eea467c3df2e6e3f7040242 | https://github.com/NVIDIA-AI-IOT-private/torch2trt/tree/953d60039e0c81e90eea467c3df2e6e3f7040242 |
FinalPool | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | CoraJung/flexible-input-slu | FinalPool | false | 17,146 | [
"Apache-2.0"
] | 7 | 6a1a6bf105f1a0c07e8d483aa6da1df7a554392d | https://github.com/CoraJung/flexible-input-slu/tree/6a1a6bf105f1a0c07e8d483aa6da1df7a554392d |
BertPredictionTransform | import math
import torch
import torch.nn as nn
import torch.cuda
import torch.distributed
def get_activation_fn(activation):
"""Return an activation function Module according to its name."""
if activation == 'gelu':
fn = GELU()
elif activation == 'relu':
fn = nn.ReLU()
elif activation ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import ... | SivilTaram/dialogue-utterance-rewriter-pytorch | BertPredictionTransform | false | 2,918 | [
"MIT"
] | 0 | 92c2254958b7a1ee9199836f7f2236575270983f | https://github.com/SivilTaram/dialogue-utterance-rewriter-pytorch/tree/92c2254958b7a1ee9199836f7f2236575270983f |
DistillLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | qinjian623/pytorch_toys | DistillLoss | false | 16,304 | [
"MIT"
] | 56 | 7f4761bddc65282ea31a2d0f9eb146772276dd7c | https://github.com/qinjian623/pytorch_toys/tree/7f4761bddc65282ea31a2d0f9eb146772276dd7c |
ResampleNorm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn.functional as F
import torch.nn as nn
import torch.functional a... | GoldbergData/pytorch-forecasting | ResampleNorm | false | 2,333 | [
"MIT"
] | 0 | e2ef3794da5d996c9740d932a4f55269bb4003f2 | https://github.com/GoldbergData/pytorch-forecasting/tree/e2ef3794da5d996c9740d932a4f55269bb4003f2 |
HandTypeLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | DuinoDu/InterHand2.6M.pl | HandTypeLoss | false | 5,093 | [
"MIT"
] | 1 | 2d216960cf95b066a197a9b49795840b1ecfd0c1 | https://github.com/DuinoDu/InterHand2.6M.pl/tree/2d216960cf95b066a197a9b49795840b1ecfd0c1 |
My_loss_offset | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | H-Liu1997/Pytorch_Pose_Estimation_Framework | My_loss_offset | false | 5,249 | [
"MIT"
] | 1 | 06616b3459ff639f8486e6ea4f93922597788b2a | https://github.com/H-Liu1997/Pytorch_Pose_Estimation_Framework/tree/06616b3459ff639f8486e6ea4f93922597788b2a |
F_fully_convolutional | import torch
import torch.nn as nn
import torch.nn.functional as F
class F_fully_convolutional(nn.Module):
def __init__(self, in_channels, out_channels, internal_size=256,
kernel_size=3, leaky_slope=0.02):
super().__init__()
pad = kernel_size // 2
self.leaky_slope = leaky_slope
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Xenovortex/INN_Embedding_Classification | F_fully_convolutional | false | 1,266 | [
"MIT"
] | 0 | df31ec3dcf70780cae5140a69ffafdd64f218e5f | https://github.com/Xenovortex/INN_Embedding_Classification/tree/df31ec3dcf70780cae5140a69ffafdd64f218e5f |
BottleneckLSTMCell | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import logging
import torch.n... | alejodosr/adaptive-inattention | BottleneckLSTMCell | false | 6,172 | [
"MIT"
] | 1 | ad1c883081e5248704be5ce5c4baa24b2eda1c59 | https://github.com/alejodosr/adaptive-inattention/tree/ad1c883081e5248704be5ce5c4baa24b2eda1c59 |
CA | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | sithu31296/image_classification | CA | false | 16,482 | [
"MIT"
] | 57 | 6b8cbce96100225621cee3166a73e852ba216cc3 | https://github.com/sithu31296/image_classification/tree/6b8cbce96100225621cee3166a73e852ba216cc3 |
Attention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | pandeydeep9/Attentive-Neural-Process | Attention | false | 12,872 | [
"Apache-2.0"
] | 0 | 7bbdc46d51ab0c891067e508d00a029c07d04802 | https://github.com/pandeydeep9/Attentive-Neural-Process/tree/7bbdc46d51ab0c891067e508d00a029c07d04802 |
SilogLoss | import torch
import torch.nn as nn
class SilogLoss(nn.Module):
def __init__(self, ratio=10, ratio2=0.85):
super().__init__()
self.ratio = ratio
self.ratio2 = ratio2
def forward(self, pred, gt):
log_diff = torch.log(pred * self.ratio) - torch.log(gt * self.ratio)
silog... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | aycatakmaz/packnet-sfm | SilogLoss | false | 12,130 | [
"MIT"
] | 0 | d89cae81290133f136f6a1d1e288affc67eed1f7 | https://github.com/aycatakmaz/packnet-sfm/tree/d89cae81290133f136f6a1d1e288affc67eed1f7 |
BertAttention | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | mcao516/SSKD-TinyBERT | BertAttention | false | 12,783 | [
"Apache-2.0"
] | 0 | d862002e03df5cb54a80657e41a77f1b6f7732d9 | https://github.com/mcao516/SSKD-TinyBERT/tree/d862002e03df5cb54a80657e41a77f1b6f7732d9 |
L2Normalization | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Jiangtong-Li/ZHSIR | L2Normalization | false | 17,502 | [
"Apache-2.0"
] | 8 | fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 | https://github.com/Jiangtong-Li/ZHSIR/tree/fd2c0a7e79f22cbf565ccd5e13342f1b317ac9b7 |
CoxPHLossSorted | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import Tens... | rohanshad/pycox | CoxPHLossSorted | false | 16,334 | [
"BSD-2-Clause"
] | 449 | 5483489d21f3441e53f78f9f8898ce607f41c632 | https://github.com/rohanshad/pycox/tree/5483489d21f3441e53f78f9f8898ce607f41c632 |
L2Norm | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | CVHj/torchcv | L2Norm | false | 13,447 | [
"MIT"
] | 433 | 6291f3e1e4bbf6467fd6b1e79001d34a59481bb6 | https://github.com/CVHj/torchcv/tree/6291f3e1e4bbf6467fd6b1e79001d34a59481bb6 |
Net | import torch
import torch.nn as tnn
class Net(tnn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = tnn.Conv2d(3, 6, 5)
self.pool = tnn.MaxPool2d(2, 2)
self.conv2 = tnn.Conv2d(6, 16, 5)
self.fc1 = tnn.Linear(16 * 5 * 5, 120)
self.fc2 = tnn.Linea... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as tnn
assert... | Jittor/Jittor | Net | false | 17,504 | [
"Apache-2.0"
] | 4 | bc945bae94bded917214b0afe12be6bf5b919dbe | https://github.com/Jittor/Jittor/tree/bc945bae94bded917214b0afe12be6bf5b919dbe |
TVLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | jokingbear/DM | TVLoss | false | 6,984 | [
"MIT"
] | 1 | 9c4dada1756f3d866455a397511d4f3bacfadc60 | https://github.com/jokingbear/DM/tree/9c4dada1756f3d866455a397511d4f3bacfadc60 |
Norm | import torch
import torch.nn as nn
class Norm(nn.Module):
def __init__(self, d_model, eps=1e-06):
super().__init__()
self.size = d_model
self.alpha = nn.Parameter(torch.ones(self.size))
self.bias = nn.Parameter(torch.zeros(self.size))
self.eps = eps
def forward(self, ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | msank00/miniTransformer | Norm | false | 12,802 | [
"MIT"
] | 0 | a264f30982d9e2dbf8c796d495f7a237c0dd53ef | https://github.com/msank00/miniTransformer/tree/a264f30982d9e2dbf8c796d495f7a237c0dd53ef |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 1024)
self.l2 = nn.Linear(1024, 512)
self.l3 = nn.Linear(512, 256)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | ctoto93/TD3 | Critic | false | 9,962 | [
"MIT"
] | 0 | 88482b9f1fb4441d74426ece60d5da13414aeb77 | https://github.com/ctoto93/TD3/tree/88482b9f1fb4441d74426ece60d5da13414aeb77 |
ISAB | import math
import torch
import torch.nn.functional as F
import torch.nn as nn
class MAB(nn.Module):
def __init__(self, dim_X, dim_Y, dim, num_heads=4, ln=False, p=None):
super().__init__()
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_X, dim)
self.fc_k = nn.Linear(dim_Y, d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | OpenXAIProject/dac | ISAB | false | 8,704 | [
"MIT"
] | 17 | 652776e21b56dcb68839363bb077d5c5ea28d81e | https://github.com/OpenXAIProject/dac/tree/652776e21b56dcb68839363bb077d5c5ea28d81e |
ChannelwiseAttention | import torch
import torch.nn as nn
import torch.nn.functional as F
class ChannelwiseAttention(nn.Module):
def __init__(self, in_channels):
super(ChannelwiseAttention, self).__init__()
self.in_channels = in_channels
self.linear_1 = nn.Linear(self.in_channels, self.in_channels // 4)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | farkoo/novel-seam-carving-method | ChannelwiseAttention | false | 3,495 | [
"MIT"
] | 0 | aa3e9a4e3d5e13872eed412444e5be519542f7e5 | https://github.com/farkoo/novel-seam-carving-method/tree/aa3e9a4e3d5e13872eed412444e5be519542f7e5 |
MultiLevelPooling | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | Asichurter/Few-Shot-Project | MultiLevelPooling | false | 61 | [
"MIT"
] | 0 | 865cd6aa7b996c518dfa48dcc9ffad90445f9efe | https://github.com/Asichurter/Few-Shot-Project/tree/865cd6aa7b996c518dfa48dcc9ffad90445f9efe |
CNN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | jianzhnie/TsFormer | CNN | false | 3,731 | [
"Apache-2.0"
] | 0 | 47e362f02445ba00d5ab8db206667767e72faca7 | https://github.com/jianzhnie/TsFormer/tree/47e362f02445ba00d5ab8db206667767e72faca7 |
ContrastiveEmbeddingLoss | import torch
import torch.nn as nn
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.distributed
class ContrastiveEmbeddingLoss(nn.Module):
"""
Contrastive embedding loss
paper: http://yann.lecun.com/exdb/publi... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
from t... | asmekal/catalyst | ContrastiveEmbeddingLoss | false | 12,123 | [
"MIT"
] | 0 | e11365c0a9812649ceaef14e53061cd5117d8684 | https://github.com/asmekal/catalyst/tree/e11365c0a9812649ceaef14e53061cd5117d8684 |
BertPredictionHead | from _paritybench_helpers import _mock_config
import math
import torch
import torch.utils.data
import torch.nn as nn
import torch
import torch.nn.parallel
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT"s gelu is slightly different (and gives slightly different ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | adymaharana/VLCStoryGan | BertPredictionHead | false | 18,257 | [
"MIT"
] | 10 | 74112404689e8144c2ed2d375e1e5a1cde09debb | https://github.com/adymaharana/VLCStoryGan/tree/74112404689e8144c2ed2d375e1e5a1cde09debb |
ResidualBlock_noBN | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | juyongjiang/Simple-SR | ResidualBlock_noBN | false | 7,009 | [
"MIT"
] | 1 | 76820511abc04fbe6e4a79d23c67aee97406d563 | https://github.com/juyongjiang/Simple-SR/tree/76820511abc04fbe6e4a79d23c67aee97406d563 |
SimpleNotModule | import torch
import torch.jit
import torch.onnx
import torch.nn
class SimpleNotModule(torch.nn.Module):
def __init__(self):
super(SimpleNotModule, self).__init__()
def forward(self, a):
b = torch.logical_not(a)
return torch.logical_not(b)
def get_inputs():
return [torch.rand([4... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.jit
import torch.onnx
import torch.nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | YaronBenAtar/glow | SimpleNotModule | false | 14,657 | [
"Apache-2.0"
] | 2,838 | a13706a4239fa7eaf059c670dc573e3eb0768f86 | https://github.com/YaronBenAtar/glow/tree/a13706a4239fa7eaf059c670dc573e3eb0768f86 |
MinibatchStdDev | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | leogeier/dl-2020-prog-gan | MinibatchStdDev | false | 3,901 | [
"MIT"
] | 0 | 12f28353548188af31cc14ee18a5444ad3d95a0c | https://github.com/leogeier/dl-2020-prog-gan/tree/12f28353548188af31cc14ee18a5444ad3d95a0c |
ReshapeF | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch
import torch.nn as nn
assert_size_stride =... | bomtorazek/contrastive-unpaired-translation | ReshapeF | false | 12,210 | [
"BSD-3-Clause"
] | 0 | 07c048038375e1b9a4e464154b8dbc49f5e16ede | https://github.com/bomtorazek/contrastive-unpaired-translation/tree/07c048038375e1b9a4e464154b8dbc49f5e16ede |
RNN | import torch
import torch.nn as nn
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
self.i2o = nn.Linear(input_size + hidden_size, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | LatifB/char-level-classification | RNN | false | 9,217 | [
"MIT"
] | 0 | 3d0e21e85571efafe0e26c6f27c5fa258a9503da | https://github.com/LatifB/char-level-classification/tree/3d0e21e85571efafe0e26c6f27c5fa258a9503da |
ConvGLU | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
import torch.utils.data
import torch.cuda
import torch.opti... | Malkovsky/NeMo | ConvGLU | false | 2,608 | [
"Apache-2.0"
] | 0 | 8cf9aad8ecba36f1bd7b096cf274c2bc8ac695c3 | https://github.com/Malkovsky/NeMo/tree/8cf9aad8ecba36f1bd7b096cf274c2bc8ac695c3 |
BCEDiceLoss | # AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _al... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | DLWK/EANet | BCEDiceLoss | false | 7,953 | [
"MIT"
] | 14 | 3680e099dd815117d4a54f928fb8247aa2f0b71a | https://github.com/DLWK/EANet/tree/3680e099dd815117d4a54f928fb8247aa2f0b71a |
Network | # AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _alig... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | georgeyiasemis/Deep-Reinforcement-Learning-on-a-Grid-World- | Network | false | 6,739 | [
"MIT"
] | 1 | f32ceac5f4e29cba212d6fd1b8a25c08ac733666 | https://github.com/georgeyiasemis/Deep-Reinforcement-Learning-on-a-Grid-World-/tree/f32ceac5f4e29cba212d6fd1b8a25c08ac733666 |
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